Quantitative techniques in finance

Lecturer:

Professor Robert Cressy

Module description

This module builds on the foundations provided in Financial Modelling and Forecasting Techniques provided in the 1 ^st term and aims to provide students with a wider range of econometric and financial economics skills that will be useful in writing the Dissertation. It focuses on the one hand in using real world data to estimate equations and to test hypotheses, and on the other on financial economic modeling to enable the student to read high level papers in the literature.

Learning outcomes

By the end of the course you should be able to:

• specify, estimate and evaluate a range of regression models and techniques in the finance field
• understand and be able to apply in a finance context
• • limited dependent variable models
  • panel data models
  • matched sample methods (propensity score)
  • Heckman sample selection methods to deal with biased estimates in the presence of missing data
• understand and apply methods of consumer and portfolio optimisation
• understand and apply methods for calculating insurance premia
• understand some simple applications of game theory to financial model building

Assessment

• 1 group assignment (approx 2,000 words) to be submitted by March/April (25%)
• 2-hour exam in May/June (75%)